Towards a cashless society: Use of electronic payment devices among generation Z
Bibliographic record
Abstract
Nowadays, transactions on e-commerce platforms (e-payment) utilizing a credit card are popular. Using credit cards for electronic purchases over the Internet is much different from offline purchases in traditional stores; only online transactions do not include either physical credit card or a signature. The e-payment has become a common mode of payment for online transactions made. It is an electronic billing system that gives clients the ability to make payments using the Internet. The objective of this paper was to analyze the associations among continuance intention e-payment, effort expectancy, facilitating conditions, performance expectancy, social influence, and actual usage of e-payment. The data was tested empirically on data collected from 667 Generation Z e-payment users in Malaysia. The results found that facilitating conditions, performance expectancy, and social influence impacted the actual usage of e-payment. Surprisingly, effort expectancy was not significantly associated with the actual e-payment usage. The findings of this study have several implications for managers and point the way towards future research. No prior empirical study has investigated the role of the Unified Theory of Acceptance and Use of Technology model on e-payment usage among Generation Z in Malaysia to the best of the authors’ knowledge. These results provide valuable contributions that can help decision-makers formulate or adjust their strategies associated with e-payments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".